{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/42366"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/42366","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mining heterogeneous information networks","abstract":"Real-world physical objects and abstract data entities are interconnected, forming gigantic networks. By structuring these objects and their interactions into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In my thesis, I investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) ranking-based clustering, (2) meta-path-based similarity search and mining, (3) user-guided relation strength-aware mining, and many other potential developments. This thesis introduces this new research frontier and points out some promising research directions.","abstract_html":"Real-world physical objects and abstract data entities are interconnected, forming gigantic networks. By structuring these objects and their interactions into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In my thesis, I investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) ranking-based clustering, (2) meta-path-based similarity search and mining, (3) user-guided relation strength-aware mining, and many other potential developments. This thesis introduces this new research frontier and points out some promising research directions.","abstract_has_math":false,"creators":["Sun, Yizhou"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei","Zhai, ChengXiang","Roth, Dan","Aggarwal, Charu C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-02-03T19:36:32Z","date_published":"2013-02-03T19:36:32Z","updated_at":"2026-07-22T22:25:33Z","subjects":["information network","social network","heterogeneous information network","data mining","network schema","meta-path","clustering","ranking","similarity search","relationship prediction","user-guided meta-path selection","relation strength-aware mining"],"languages":["en"],"rights":["Copyright 2012 Yizhou Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/42366","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Zhai, ChengXiang","Roth, Dan","Aggarwal, Charu C."]},{"key":"dc:creator","label":"Author","values":["Sun, Yizhou"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-02-03T19:36:32Z","2012-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["information network","social network","heterogeneous information network","data mining","network schema","meta-path","clustering","ranking","similarity search","relationship prediction","user-guided meta-path selection","relation strength-aware mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2012 Yizhou Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/42366"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Real-world physical objects and abstract data entities are interconnected, forming gigantic networks. 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This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) ranking-based clustering, (2) meta-path-based similarity search and mining, (3) user-guided relation strength-aware mining, and many other potential developments. This thesis introduces this new research frontier and points out some promising research directions.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-11-26T15:58:15Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Sun_Yizhou.pdf: 2648559 bytes, checksum: 131bbd8e442f161160beb11b44cd5420 (MD5)","Made available in DSpace on 2013-02-03T19:36:32Z (GMT). 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Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In my thesis, I investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) ranking-based clustering, (2) meta-path-based similarity search and mining, (3) user-guided relation strength-aware mining, and many other potential developments. 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